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Erwerbstätigkeit in Deutschland im europäischen Vergleich

Author

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  • Sven Schreiber

    (Macroeconomic Policy Institute (IMK))

Abstract

Im vorliegenden Report werden die Erwerbstätigenquoten europäischer Länder untersucht, um Teilzeiteffekte bereinigt und nach Geschlechtern aufgeschlüsselt. Die positive Entwicklung des deutschen Arbeitsmarkts seit 2005 bestätigt sich und ist (beinahe) unabhängig von Teilzeiteffekten. Jedoch täuscht das nominell hohe Niveau der Erwerbstätigkeit durch den vergleichsweise hohen Teilzeitanteil und den geringen Stundenumfang der Teilzeitstellen in Deutschland. Dementsprechend liegt bei den korrigierten Erwerbstätigenquoten (in Vollzeitäquivalenten) Deutschland in Europa derzeit auf Platz 11 statt wie bei den nominellen Quoten auf Platz 5. Die Länder, bei denen die teilzeitbedingte Korrektur groß ist, weisen tendenziell eine geringere korrigierte Erwerbstätigenquote von Frauen im Vergleich zu Männern auf. Eine alternative Erwerbstätigenquote unter Berücksichtigung der Erwerbsneigung führt zu keiner grundlegend veränderten Beurteilung.

Suggested Citation

  • Sven Schreiber, 2015. "Erwerbstätigkeit in Deutschland im europäischen Vergleich," IMK Report 103-2015, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    • Handle: RePEc:imk:report:103-2015
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    References listed on IDEAS

    as
    1. Bai, Jushan & Ng, Serena, 2013. "Principal components estimation and identification of static factors," Journal of Econometrics, Elsevier, vol. 176(1), pages 18-29.
    2. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
    3. Böhm, Kathrin, 2011. "Schätzung der Stillen Reserve mit dem Mikrozensuspanel 2001-2004 : eine Machbarkeitsstudie," IAB-Forschungsbericht 201102, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
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    Cited by:

    1. Raddatz, Guido, 2015. "Mehr Arbeit wagen," Argumente zur Marktwirtschaft und Politik 129, Stiftung Marktwirtschaft / The Market Economy Foundation, Berlin.
    2. Florian Blank & Camille Logeay & Erik Türk & Josef Wöss & Rudolf Zwiener, 2018. "Den demografischen Wandel bewältigen: Die Schlüsselrolle des Arbeitsmarktes," IMK Report 137-2018, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    3. Lara Minkus, 2019. "Labor Market Closure and the Stalling of the Gender Pay Gap," SOEPpapers on Multidisciplinary Panel Data Research 1049, DIW Berlin, The German Socio-Economic Panel (SOEP).

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